Because the task for discriminator is much easier, its CNN backbone is from Resnet18 and the number of layers in the encoder and decoder are both reduced to four. Only one FC is set behind the decoder judging whether the input image-layout pairs are true or not. We choose hinge loss [1]}, [2]} \(L_{adv}\)  to train the discriminator. And the overall loss is the weighted sum of \(L_{adv}\)  and \(L_{rec}\) .
